marginal_rxx: Function to calculate the marginal reliability

In mirt: Multidimensional Item Response Theory

Function to calculate the marginal reliability

Description

Given an estimated model and a prior density function, compute the marginal reliability (Thissen and Wainer, 2001). This is only available for unidimensional tests.

Usage

marginal_rxx(mod, density = dnorm, var_theta = 1, ...)

Arguments

mod	an object of class 'SingleGroupClass'
density	a density function to use for integration. Default assumes the latent traits are from a normal (Gaussian) distribution
var_theta	variance of the Theta distribution (typically 1 for many fitted IRT models)
...	additional arguments passed to the density function

Author(s)

Phil Chalmers [e-mail]

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v048.i06")}

Thissen, D. and Wainer, H. (2001). Test Scoring. Lawrence Erlbaum Associates.

See Also

empirical_rxx, extract.group, testinfo

Examples

dat <- expand.table(deAyala)
mod <- mirt(dat, 1)

# marginal estimate
marginal_rxx(mod)

## Not run: 

# empirical estimate (assuming the same prior)
fscores(mod, returnER = TRUE)

# empirical rxx the alternative way, given theta scores and SEs
fs <- fscores(mod, full.scores.SE=TRUE)
head(fs)
empirical_rxx(fs)


## End(Not run)

mirt documentation built on Oct. 17, 2023, 5:06 p.m.